Sex differences in the functional topography of association networks in youth

Shanmugan, Sheila; Seidlitz, Jakob; Cui, Zaixu; Adebimpe, Azeez; Bassett, Dani S.; Bertolero, Maxwell A.; Davatzikos, Christos; Fair, Damien A.; Gur, Raquel E.; Gur, Ruben; Larsen, Bart; Li, Hongming; Pines, Adam; Raznahan, Armin; Roalf, David R.; Shinohara, Russell T.; Vogel, Jacob W.; Wolf, Daniel H.; Fan, Yong; Alexander-Bloch, Aaron; Satterthwaite, Theodore D.

doi:10.1073/pnas.2110416119

Cited by 26 publications

(17 citation statements)

References 97 publications

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“…The general lack of predictability of internalizing behaviors seen here and in prior work may be related to individual differences in the signal-to-noise ratio in the associations between functional connectivity and the behaviors themselves. Furthermore, the presence of significant predictions of internalizing behaviors in females, but not in males, may be underscored by the earlier development of functional networks, and especially the heteromodal association networks, in females during childhood (7,62). The delayed development of association networks-which drive these behavioral predictions-paired with the lower levels of internalizing behaviors observed in males, could in part explain the lower observed accuracies in males.…”

Section: Discussionmentioning

confidence: 99%

Brain-based predictions of psychiatric illness-linked behaviors across the sexes

Dhamala

Ooi

Chen

et al. 2022

Preprint

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Background: Individual differences in functional brain connectivity can be used to predict both the presence of psychiatric illness and variability in associated behaviors. However, despite evidence for sex differences in functional network connectivity and in the prevalence, presentation, and trajectory of psychiatric illnesses, the extent to which disorder-relevant aspects of network connectivity are shared or unique across the sexes remains to be determined. Methods: In this work, we used predictive modelling approaches to evaluate whether shared or unique functional connectivity correlates underlie the expression of psychiatric illness-linked behaviors in males and females in data from the Adolescent Brain Cognitive Development study (n=5260; 2571 females). Results: We demonstrate that functional connectivity profiles predict individual differences in externalizing behaviors in males and females, but only predict internalizing behaviors in females. Furthermore, models trained to predict externalizing behaviors in males generalize to predict internalizing behaviors in females, and models trained to predict internalizing behaviors in females generalize to predict externalizing behaviors in males. Finally, the neurobiological correlates of many behaviors are largely shared within and across sexes: functional connections within and between heteromodal association networks including default, limbic, control, and dorsal attention networks are associated with internalizing and externalizing behaviors as well as attentional deficits. Conclusions: Taken together, these findings suggest that shared neurobiological patterns may manifest as distinct behaviors across the sexes. These results highlight the need to consider factors beyond just neurobiology in the diagnosis and treatment of psychiatric illnesses.

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Section: Discussionmentioning

confidence: 99%

Brain-based predictions of psychiatric illness-linked behaviors across the sexes

Dhamala

Ooi

Chen

et al. 2022

Preprint

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“…Functional networks are bounded by the anatomical structure of neural connections (Xie et al, 2021 ). The topology of functional networks is dependent on individual development (Shanmugan et al, 2022 ). Furthermore, Functional connectivity (FC) can be used to predict behavioral traits such as fluid intelligence or even personality factors (NEO-FFI; Li et al, 2022 ).…”

Section: Brain Functional Networkmentioning

confidence: 99%

The role of the salience network in cognitive and affective deficits

Schimmelpfennig

Topczewski

Zajkowski

et al. 2023

Front. Hum. Neurosci.

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Analysis and interpretation of studies on cognitive and affective dysregulation often draw upon the network paradigm, especially the Triple Network Model, which consists of the default mode network (DMN), the frontoparietal network (FPN), and the salience network (SN). DMN activity is primarily dominant during cognitive leisure and self-monitoring processes. The FPN peaks during task involvement and cognitive exertion. Meanwhile, the SN serves as a dynamic “switch” between the DMN and FPN, in line with salience and cognitive demand. In the cognitive and affective domains, dysfunctions involving SN activity are connected to a broad spectrum of deficits and maladaptive behavioral patterns in a variety of clinical disorders, such as depression, insomnia, narcissism, PTSD (in the case of SN hyperactivity), chronic pain, and anxiety, high degrees of neuroticism, schizophrenia, epilepsy, autism, and neurodegenerative illnesses, bipolar disorder (in the case of SN hypoactivity). We discuss behavioral and neurological data from various research domains and present an integrated perspective indicating that these conditions can be associated with a widespread disruption in predictive coding at multiple hierarchical levels. We delineate the fundamental ideas of the brain network paradigm and contrast them with the conventional modular method in the first section of this article. Following this, we outline the interaction model of the key functional brain networks and highlight recent studies coupling SN-related dysfunctions with cognitive and affective impairments.

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“…Multiscale functional connectivity measures were computed from each preprocessed rsfMRI scan based on functional networks (FNs) obtained using a personalized functional network computational method (Li et al, 2017; Cui et al, 2020). We computed personalized FNs for each individual subject using a group-sparsity regularized non-negative matrix factorization (NMF) method (Li et al, 2017; Cui et al, 2020), which has been successfully adopted in multiple recent studies for computing personalized FNs (Cui et al, 2022; Pines et al, 2022; Shanmugan et al, 2022). Particularly, we first computed group-level FNs using a normalized-cuts based spectral clustering method to identify representative FNs from 50 sets of group-level FNs, each set being computed on a subset of 150 subjects randomly selected from each of the sites with a probability proportional to the sample sizes of different sites.…”

Section: Methodsmentioning

confidence: 99%

Multiscale functional connectivity patterns of the aging brain learned from rsfMRI data of 4,259 individuals of the multi-cohort iSTAGING study

Zhou

Srinivasan

et al. 2022

Preprint

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To learn multiscale functional connectivity patterns of the aging brain, we built a brain age prediction model of functional connectivity measures at seven scales on a large fMRI dataset, consisting of resting-state fMRI scans of 4259 individuals with a wide age range (22 to 97 years, with an average of 63) from five cohorts. We computed multiscale functional connectivity measures of individual subjects using a personalized functional network computational method, harmonized the functional connectivity measures of subjects from multiple datasets in order to build a functional brain age model, and finally evaluated how functional brain age gap correlated with cognitive measures of individual subjects. Our study has revealed that functional connectivity measures at multiple scales were more informative than those at any single scale for the brain age prediction, the data harmonization significantly improved the brain age prediction performance, and harmonization in the tangent space worked better than in the original space. Moreover, brain age gap scores of individual subjects derived from the brain age prediction model were significantly correlated with clinical and cognitive measures. Overall, these results demonstrated that multiscale functional connectivity patterns learned from a large-scale multi-site rsfMRI dataset were informative for characterizing the aging brain and the derived brain age gap was predictive of cognitive and clinical measures.

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Sex differences in the functional topography of association networks in youth

Cited by 26 publications

References 97 publications

Brain-based predictions of psychiatric illness-linked behaviors across the sexes

Brain-based predictions of psychiatric illness-linked behaviors across the sexes

The role of the salience network in cognitive and affective deficits

Multiscale functional connectivity patterns of the aging brain learned from rsfMRI data of 4,259 individuals of the multi-cohort iSTAGING study

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